Create callbacks for min_maximize_cmd

Enable VS_PROJ = true for temporary use

scripts/mk_util.py

@@ -59,7 +59,7 @@ SHOW_CPPS = True
 VS_X64 = False
 ONLY_MAKEFILES = False
 Z3PY_SRC_DIR=None
-VS_PROJ = False
+VS_PROJ = True
 TRACE = False
 DOTNET_ENABLED=False
 JAVA_ENABLED=False

src/opt/opt_cmds.cpp

@@ -10,7 +10,7 @@ Abstract:
 
 Author:
 
-    Anh-Phan Dung (t-anphan) 2013-10-14
+    Anh-Dung Phan (t-anphan) 2013-10-14
 
 Notes:
 
@@ -23,6 +23,7 @@ class opt_context {
     ast_manager& m;
     expr_ref_vector m_formulas;
     vector<rational> m_weights;
+
 public:
     opt_context(ast_manager& m):
         m(m),
@@ -43,6 +44,7 @@ class assert_weighted_cmd : public cmd {
     unsigned   m_idx;
     expr_ref   m_formula;
     rational   m_weight;
+
 public:
     assert_weighted_cmd(cmd_context& ctx, opt_context* opt_ctx):
         cmd("assert-weighted"),
@@ -60,6 +62,7 @@ public:
         m_idx = 0; 
         m_formula = 0;
     }
+
     virtual char const * get_usage() const { return "<formula> <rational-weight>"; }
     virtual char const * get_descr(cmd_context & ctx) const { return "assert soft constraint with weight"; }
     virtual unsigned get_arity() const { return 2; }
@@ -107,22 +110,39 @@ public:
 // to do the feasibility check.
 class min_maximize_cmd : public cmd {
     bool m_is_max;
+    expr_ref m_term;
     opt_context* m_opt_ctx;
+
 public:
     min_maximize_cmd(cmd_context& ctx, opt_context* opt_ctx, bool is_max):
         cmd(is_max?"maximize":"minimize"),
         m_is_max(is_max),
+        m_term(ctx.m()),
         m_opt_ctx(opt_ctx)
     {}
 
+    virtual void reset(cmd_context & ctx) { 
+        m_term = 0;
+    }
+
     virtual char const * get_usage() const { return "<term>"; }
     virtual char const * get_descr(cmd_context & ctx) const { return "check sat modulo objective function";}
     virtual unsigned get_arity() const { return 1; }
 
-    // etc. TODO: Phan, add the appropriate callbacks as a warmup.
+    virtual void prepare(cmd_context & ctx) {}
+    virtual cmd_arg_kind next_arg_kind(cmd_context & ctx) const { return CPK_EXPR; }
+
+    virtual void set_next_arg(cmd_context & ctx, expr * t) {
+        m_term = t;
+    }
+
+    virtual void failure_cleanup(cmd_context & ctx) {
+        reset(ctx);
+    }
 
     virtual void execute(cmd_context & ctx) {
-        // here is how to retrieve the soft constraints:
+        std::cout << "TODO: " << mk_pp(m_term, ctx.m()) << "\n";
+        // Here is how to retrieve the soft constraints
         m_opt_ctx->formulas();
         m_opt_ctx->weights();
         get_background(ctx);
@@ -134,14 +154,13 @@ private:
         ptr_vector<expr>::const_iterator it  = ctx.begin_assertions();
         ptr_vector<expr>::const_iterator end = ctx.end_assertions();
         for (; it != end; ++it) {
-            // need a solver object that supports soft constraints
+            // Need a solver object that supports soft constraints
             // m_solver.assert_expr(*it);
         }
     }
 
 };
 
-
 void install_opt_cmds(cmd_context & ctx) {
     opt_context* opt_ctx = alloc(opt_context, ctx.m());
     ctx.insert(alloc(assert_weighted_cmd, ctx, opt_ctx));

src/opt/opt_cmds.h

@@ -10,7 +10,7 @@ Abstract:
 
 Author:
 
-    Anh-Phan Dung (t-anphan) 2013-10-14
+    Anh-Dung Phan (t-anphan) 2013-10-14
 
 Notes:
 

src/opt/plan.txt

@@ -0,0 +1,9 @@
+Create file with command-line extensions.
+Similar to muz\fp\dl_cmds:
+ - Add command (minimize <term>)
+ - Add command (assert-weighted <expr> <weight> [:id]) the weight is a positive 
+   rational number, 1 can be handled as a special case sd not weighted SAT, 
+   but as ordinary MAXSAT (e.g., using Fu Malik algorithm. This is a sample).
+   Identifier is optional and used to group constraints together. 
+   The F# sample illustrates what is meant.
+